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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "df630e55-57dd-470a-849b-f1bc90ac719e"
}
},
"source": [
"# 1. Importing and Visualization of CIFAR-10 Dataset"
]
},
{
"cell_type": "code",
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"metadata": {
"nbpresent": {
"id": "409a1ab7-fe1d-4430-b904-7694020a6223"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# function to import CIFAR-10 data set\n",
"def unpickle(file):\n",
" import pickle\n",
" with open(file, 'rb') as fo:\n",
" dict = pickle.load(fo, encoding='bytes')\n",
" return dict\n",
"data_batch_1 = unpickle(\"./data/data_batch_1\")\n",
"data_batch_2 = unpickle(\"./data/data_batch_2\")\n",
"data_batch_3 = unpickle(\"./data/data_batch_3\")\n",
"data_batch_4 = unpickle(\"./data/data_batch_4\")\n",
"data_batch_5 = unpickle(\"./data/data_batch_5\")\n",
"test_batch = unpickle(\"./data/test_batch\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "ce2be501-0be3-4750-8207-dfc00d7db01a"
}
},
"source": [
"What is the data structure of e.g. data_batch_1 ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data_batch_1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "09a5b60b-dcbb-4f97-ab57-e4611c253e2e"
}
},
"source": [
"What are the keys of e.g. data_batch_1 ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "c874a7c9-de0c-4ccd-a0f1-8f8a3265a0b6"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys([b'batch_label', b'labels', b'data', b'filenames'])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_batch_1.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a7f910e7-0b11-453b-84d5-df6ac88ac6dd"
}
},
"source": [
"What is the data structure of data_batch_1[b'data'] ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "fe299a35-c930-4078-97b7-c9b67f42ec42"
}
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data_batch_1[b'data'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "3f978c4f-50d0-4f00-9f19-bf8744e505a3"
}
},
"source": [
"What is the data structure of data_batch_1[b'labels'] ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "46a97575-36c0-4920-a8dc-762e94239b7e"
}
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data_batch_1[b'labels'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "2fd19982-c318-4303-8042-7a5a6998d175"
}
},
"source": [
"What is the shape of data_batch_1[b'data'] ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "b012720d-81f8-455d-8ce7-bfca64a842c8"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 3072)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_batch_1[b'data'].shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "378aeda2-a547-435e-b28b-09ceb0074a53"
}
},
"source": [
"What is the size of data_batch_1[b'labels'] ?\n"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "49c776cb-c8aa-461b-a0da-4f4d38342e2e"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data_batch_1[b'labels'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "02ca495c-e6d2-48d4-9bc2-2295272d5f6f"
}
},
"source": [
"What are the first 10 elements of data_batch_1[b'labels'] ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "438920f4-774e-4e94-9b7c-30a2106d163c"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[6, 9, 9, 4, 1, 1, 2, 7, 8, 3]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_batch_1[b'labels'][:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "1e599fcd-a46c-4750-94f8-1cf4ad8fb342"
}
},
"source": [
"What is the data type of data_batch_1[b'data'] ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "7617a699-c3d5-434f-97a5-3443489ac9db"
}
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('uint8')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_batch_1[b'data'].dtype"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "067d850d-0411-4af6-8714-a79b310ca8c1"
}
},
"source": [
"Let us concatenate the batch training data"
]
},
{
"cell_type": "code",
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"metadata": {
"nbpresent": {
"id": "942f351b-b771-4375-8df2-eec28391a576"
}
},
"outputs": [],
"source": [
"X_train=np.concatenate([data_batch_1[b'data'], \n",
" data_batch_2[b'data'], \n",
" data_batch_3[b'data'], \n",
" data_batch_4[b'data'], \n",
" data_batch_5[b'data']], \n",
" axis = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "b289f0b9-b3ab-480b-9ae6-76a893980efe"
}
},
"source": [
"Let us concatenate the training labels"
]
},
{
"cell_type": "code",
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"metadata": {
"nbpresent": {
"id": "9b85b9a0-5f2b-4c68-a74f-82f1ec212215"
}
},
"outputs": [],
"source": [
"y_train=np.concatenate([data_batch_1[b'labels'] , \n",
" data_batch_2[b'labels'],\n",
" data_batch_3[b'labels'],\n",
" data_batch_4[b'labels'],\n",
" data_batch_5[b'labels']], \n",
" axis = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "d9967582-1305-4b95-948b-e75c46fc49bb"
}
},
"source": [
"Let us define the test data as X_test"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "5c85918c-f89e-4156-8cdd-ca38d14afbb9"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 3072)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test = test_batch[b'data']\n",
"X_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "48754d72-9acd-49cf-b209-737c45047284"
}
},
"source": [
"Let us cast the test labels as ndarray"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "5f913d95-aa49-4727-8c6f-5630cbf59741"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10000,)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test=np.array(test_batch[b'labels']) \n",
"y_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "f4c3aa97-0d97-4e9c-a6b3-f2d2b1f08632"
}
},
"source": [
"What is the shape of X_train ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "a0eb7a33-19c9-46e4-b471-6f7904389177"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50000, 3072)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "81d3c33f-544e-44c8-8260-e89143cc6ef1"
}
},
"source": [
"What is the shape of Y_train ?"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "d699e7a7-efc0-421f-bd8d-2d2b34a09516"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50000,)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "d0a61e44-a2a9-4dff-9849-5f6a0e232290"
}
},
"source": [
"Let us visualize an image. "
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "d817d603-7d37-4ff2-b3d1-e95875b48f8f"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.imshow(X_train[20].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"- By means of `reshape` we can convert an array from one shape to another without \n",
"copying any data. To do this, we pass a tuple indicating the new shape to the `reshape` array instance method. \n",
"\n",
"- By default, `NumPy` arrays are created in _row major_ order. Spatially this means, that if we have a two-dimensional array of data, the items in each row of the array are stored in adjacent memory locations. In the case of a three-dimensional array of data, the items along `axis=2` are stored in adjacent order. Since the first 32 entries of the array `X_train[0]` are the red channel values of the first row of the image, etc., we need to pass the tuple $(3,32,32)$ to `reshape`. In conclusion, when __reshaping__ the array, higher order dimensions are traversed _first_ (e.g. axis $ 2 $ before advancing on axis $ 1 $.) \n",
"\n",
"- `plt.imshow` needs for each inner list the values representing a pixel. Here, with \n",
"an RGB image, there are 3 values. We thus need to transpose the array : the RGB values need to be located along `axis=2`. \n",
"\n",
"- Using ndarray's `astype` method, we can cast an array from one `dtype` to another. `uint8` represents unsigned 8-bit integer types. Why 8 bits? Most displays can only render 8 bits per channel worth of color gradation. Why can they only render 8 bits/channel? Because that is about all the human eye can see."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "38cf20c0-9404-4f32-91ed-a00e910832f8"
}
},
"source": [
"We visualize some examples from the dataset.\n",
"We show a few examples of training images from each class."
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "ba3743b9-ea50-4201-ad99-5fa47e8b82fb"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 70 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"%matplotlib inline\n",
"classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"num_classes = len(classes)\n",
"samples_per_class = 7\n",
"\n",
"\n",
"\n",
"for y, cls in enumerate(classes):\n",
" idxs = np.flatnonzero(y_train == y)\n",
" idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
" for i, idx in enumerate(idxs):\n",
" plt_idx = i * num_classes + y + 1\n",
" plt.subplot(samples_per_class, num_classes, plt_idx)\n",
" plt.imshow(X_train[idx].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
" plt.axis('off')\n",
" if i == 0:\n",
" plt.title(cls)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- When iterating over a sequence we often want to keep track of the index of the \n",
"current item. Python's built-in function `enumerate` returns a sequence of $i$, value \n",
"tuples.\n",
"\n",
"- `np.flatnonzero` returns indices that are non-zero in the (flattened version of) `y_train == y`, that is, it returns the indices for the elements that are `True`.\n",
"\n",
"- `np.random.choice(..., replace=False)` generates a random sample from a given 1-D array without replacement.\n",
"\n",
"\n",
"- The `subplot()` command specifies `numrows`, `numcols`, `fignum` where `fignum` ranges from $ 1 $ to `numrows*numcols`."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "ab168c02-9867-455d-815d-c2de707e2f87"
}
},
"source": [
"# 2. K-Nearest-Neighbour Classifier"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "36557d31-2ba4-416c-8ead-a92fb7446e85"
}
},
"source": [
" We subsample the data for more efficient code execution in this exercise."
]
},
{
"cell_type": "code",
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"metadata": {
"nbpresent": {
"id": "26316896-3b01-455b-9a0a-87278f088d83"
}
},
"outputs": [],
"source": [
"num_training = 5000\n",
"mask = range(num_training)\n",
"X_train = X_train[mask]\n",
"y_train = y_train[mask]\n",
"\n",
"num_test = 500\n",
"mask = range(num_test)\n",
"X_test = X_test[mask]\n",
"y_test = y_test[mask]"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "27db2b6f-c417-4d15-bff4-8c00d58cb808"
}
},
"source": [
"We define Class KNearestNeighbor."
]
},
{
"cell_type": "code",
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"metadata": {
"nbpresent": {
"id": "497fbf77-9a17-4b35-a0d8-375972850902"
}
},
"outputs": [],
"source": [
"class KNearestNeighbor():\n",
" \"\"\" a kNN classifier with L2 distance \"\"\"\n",
"\n",
" def __init__(self):\n",
" pass\n",
"\n",
" def train(self, X, y):\n",
" \"\"\"\n",
" Train the classifier. For k-nearest neighbors this is just \n",
" memorizing the training data.\n",
"\n",
" Inputs:\n",
" - X: A numpy array of shape (num_train, D) containing the training data\n",
" consisting of num_train samples each of dimension D.\n",
" - y: A numpy array of shape (N,) containing the training labels, where\n",
" y[i] is the label for X[i].\n",
" \"\"\"\n",
" self.X_train = X.astype('float')\n",
" self.y_train = y\n",
" \n",
" def predict(self, X, k=1, num_loops=0):\n",
" \"\"\"\n",
" Predict labels for test data using this classifier.\n",
"\n",
" Inputs:\n",
" - X: A numpy array of shape (num_test, D) containing test data consisting\n",
" of num_test samples each of dimension D.\n",
" - k: The number of nearest neighbors that vote for the predicted labels.\n",
" - num_loops: Determines which implementation to use to compute distances\n",
" between training points and testing points.\n",
"\n",
" Returns:\n",
" - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
" test data, where y[i] is the predicted label for the test point X[i]. \n",
" \"\"\"\n",
" if num_loops == 0:\n",
" dists = self.compute_distances_no_loops(X)\n",
" elif num_loops == 1:\n",
" dists = self.compute_distances_one_loop(X)\n",
" elif num_loops == 2:\n",
" dists = self.compute_distances_two_loops(X)\n",
" else:\n",
" raise ValueError('Invalid value %d for num_loops' % num_loops)\n",
"\n",
" return self.predict_labels(dists, k=k)\n",
"\n",
" def compute_distances_two_loops(self, X):\n",
" \"\"\"\n",
" Compute the distance between each test point in X and each \n",
" training point in self.X_train using a nested loop over both \n",
" the training data and the test data.\n",
"\n",
" Inputs:\n",
" - X: A numpy array of shape (num_test, D) containing test data.\n",
"\n",
" Returns:\n",
" - dists: A numpy array of shape (num_test, num_train) where \n",
" dists[i, j] is the Euclidean distance between the ith test \n",
" point and the jth training point.\n",
" \"\"\"\n",
" num_test = X.shape[0]\n",
" num_train = self.X_train.shape[0]\n",
" dists = np.zeros((num_test, num_train))\n",
" X = X.astype('float')\n",
" for i in range(num_test):\n",
" for j in range(num_train):\n",
" dists[i, j] = np.sqrt(np.sum(np.square(self.X_train[j,:] - X[i,:])))\n",
" \n",
" return dists\n",
"\n",
" def compute_distances_one_loop(self, X):\n",
" \"\"\"\n",
" Compute the distance between each test point in X and each training point\n",
" in self.X_train using a single loop over the test data.\n",
"\n",
" Input / Output: Same as compute_distances_two_loops\n",
" \"\"\"\n",
" num_test = X.shape[0]\n",
" num_train = self.X_train.shape[0]\n",
" dists = np.zeros((num_test, num_train))\n",
" X = X.astype('float')\n",
" for i in range(num_test):\n",
" dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))\n",
" \n",
" \n",
" return dists\n",
"\n",
" def compute_distances_no_loops(self, X):\n",
" \"\"\"\n",
" Compute the distance between each test point in X and each training point\n",
" in self.X_train using no explicit loops.\n",
"\n",
" Input / Output: Same as compute_distances_two_loops\n",
" \"\"\"\n",
" num_test = X.shape[0]\n",
" num_train = self.X_train.shape[0]\n",
" dists = np.zeros((num_test, num_train)) \n",
" X=X.astype('float')\n",
" \n",
" # Most \"elegant\" solution leads however to memory issues\n",
" # dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2)).T\n",
" # split (p-q)^2 to p^2 + q^2 - 2pq\n",
" dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))\n",
" \n",
" \n",
" \n",
" return dists\n",
"\n",
" def predict_labels(self, dists, k=1):\n",
" \"\"\"\n",
" Given a matrix of distances between test points and training points,\n",
" predict a label for each test point.\n",
"\n",
" Inputs:\n",
" - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
" gives the distance betwen the ith test point and the jth training point.\n",
"\n",
" Returns:\n",
" - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
" test data, where y[i] is the predicted label for the test point X[i]. \n",
" \"\"\"\n",
" num_test = dists.shape[0]\n",
" y_pred = np.zeros(num_test, dtype='float64')\n",
" for i in range(num_test):\n",
" # A list of length k storing the labels of the k nearest neighbors to\n",
" # the ith test point.\n",
" closest_y = []\n",
" # get the k indices with smallest distances\n",
" min_indices = np.argsort(dists[i,:])[:k] \n",
" closest_y = np.bincount(self.y_train[min_indices])\n",
" # predict the label of the nearest example\n",
" y_pred[i] = np.argmax(closest_y) \n",
"\n",
" return y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Methods within a class are defined in much the same way as functions, that is, using `def`. Every defined class has a _special method_ called `__init__()` `Python` runs automatically whenever we create a new _instance_ based on the `NearestNeighbor` \n",
"class. The `self` parameter is required in the method definition, and it must come first \n",
"before the other parameters. It must be included in the definition because when `Python` \n",
"calls this `__init__` method later (to create an instance of `NearestNeighbor`), \n",
"the method call will automatically pass the `self` argument. Every method call associated with a class automatically passes `self`, which is a reference to the instance itself; \n",
"it gives the individual instance access to the attributes and methods in the class. In summary, when you invoke a class method on an object instance, `Python` \n",
"arranges for the first argument to be the invoking object instance, which is always \n",
"assigned to each method's `self` argument.\n",
"\n",
"- The two variables `self.X_train` and `self.y_train` each have the prefix `self`. Any variable prefixed with `self` is available to every method in the class, and we will also be able to access these variables through any instance created from the class. `self.X_train = X` takes the value stored in the parameter `X` and stores it in the variable `self.X_train`, which is then attached to the instance being created. The same process happens with `self.y_train = y`. \n",
"\n",
"- Variables that are accessible through instances like this are called _attributes_.\n",
"`np.zeros` produces an array of $ 0 $'s, here the size is `num_test`.\n",
"\n",
"- `np.sum` returns the sum of all elements in the array or along an axis. Zero-length \n",
"arrays have sum $ $ 0.\n",
"\n",
"- `np.argmin` returns the index of minimum element.\n",
"\n",
"- When Python reads the line `nn = NearestNeighbor()`, it calls the `__init__()` method in `NearestNeighbor()`. The `__init__()` method creates an instance representing this particular nearest neighbor classifier. The `__init__()` method has no explicit return statement, but Python automatically returns an instance representing this nearest neighbor classifier. We store that instance in the variable `nn`. The naming convention is helpful here: we can usually assume that a capitalized name like `NearestNeighbor()` refers to a class, and a lowercase name like `nn` refers to a single instance created from a class.\n",
"\n",
"\n",
"- `np.argsort` returns the indices that would sort an array.\n",
"\n",
"- `np.bincount(x)` counts the number of occurrences of each value in an array of non-negative integers. The number of bins (of size 1) is one larger than the largest value in $ x $.\n",
"\n",
"- Instead of a loop that contains a _broadcasting_ process with a 2D array \n",
"\n",
"`for i in range(num_test):\n",
" dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))`\n",
"\n",
"we can speed up this process by broadcasting with a 3D array \n",
"\n",
"`dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2))`\n",
"\n",
"- The _Broadcasting rule_ states: two arrays are compatible for broadcasting if for each \n",
"_trailing dimension_ (that is, starting from the end of an array), the axis lengths match or if either of the lengths is 1. In the case of `self._train[:, np.newaxis, :]`, the trailing dimension is $2$ and the axis length $D$. The trailing dimension of `X` is 1 and has axis length $D$. Thus, they match. Broadcasting is then performed over the missing and / or length $ 1 $ dimensions. If we need to add a new axis with length $ 1 $ specifically for broadcasting purposes, `NumPy` arrays offer a special syntax for inserting new axes by indexing. We use the special `np.newaxis` attribute along with full slices to insert the new axis. We may imagine that `num_test` copies of `self.X_train` are tiled up along `axis=1` of `self.X_train[:, np.newaxis, :]`. On the other hand, `num_train` copies of `X` are tiled up on top of each other. First, the subtraction is performed along `axis=2`. Then, it is performed along `axis=1`, and finally over `axis=0`. The resulting shape of the subtraction then is (`num_train`, `num_test`, D). Since this runs into memory issues, we need to rewrite it as follows:\n",
"\n",
"`dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))`\n",
"\n",
"which yields an array with shape (`num_test`, `num_train`, D)\n",
"\n",
"- It is important to cast the data files as `float`."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "91c8998c-f531-4774-98ca-6c9631050fd3"
}
},
"source": [
"Create an instance nn from the class KNearestNeighbor"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "215be79c-8fe0-4e10-9587-6bea172bb33a"
}
},
"outputs": [],
"source": [
"classifier = KNearestNeighbor()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "2f886096-8250-4739-8645-37950f408d41"
}
},
"source": [
"We call the method `train` of the `KNearestNeighbor` class."
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "de24c3a8-0860-446e-b974-3e0c334feced"
}
},
"outputs": [],
"source": [
"classifier.train(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "d058a8de-3c50-4514-8405-5aff67b26398"
}
},
"source": [
"We test our implementation with two_loops"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "d87bb3a8-6338-4957-ac73-4c81b87821eb"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(500, 5000)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dists = classifier.compute_distances_two_loops(X_test)\n",
"dists.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "c1277b26-a267-4dec-ab9d-e44d31cdaa3e"
}
},
"source": [
"We can visualize the distance matrix: each row is a single test example and its distances to training examples"
]
},
{
"cell_type": "code",
"metadata": {
"nbpresent": {
"id": "ae3a05a2-a3e6-4e65-a59f-0204411f57f9"
}
},